提出贝叶斯事件模型，更准识别疾病亚型与进展阶段。
Bayesian Event-Based Model for Disease Subtype and Stage Inference
- 基于贝叶斯框架重构事件模型，提升亚型推断稳定性。
- 在合成数据中显著优于传统SuStaIn模型，尤其在排序与分型任务。
- 真实阿尔茨海默病数据验证结果更符合医学共识。
慢性病在患者间常呈现不同进展模式，但通常存在少数结构性亚型。为捕捉这种异质性，已有方法如基于事件的亚型与阶段推断模型（SuStaIn）可从横断面数据中估计亚型数量、各亚型的疾病进展顺序，并将患者分配至对应亚型。然而其鲁棒性尚不明确。本文提出一种原则性的贝叶斯事件模型（BEBMS），并在多种具有不同程度模型误设的合成数据上与SuStaIn进行对比。实验表明，BEBMS在排序、分阶段和亚型分配任务中均显著优于SuStaIn。进一步应用于真实阿尔茨海默病数据集后，BEBMS的结果更符合该病进展的科学共识。
原文摘要 · Abstract (English)
Chronic diseases often progress differently across patients. Rather than randomly varying, there are typically a small number of subtypes for how a disease progresses across patients. To capture this structured heterogeneity, the Subtype and Stage Inference Event-Based Model (SuStaIn) estimates the number of subtypes, the order of disease progression for each subtype, and assigns each patient to a subtype from primarily cross-sectional data. It has been widely applied to uncover the subtypes of many diseases and inform our understanding of them. But how robust is its performance? In this paper, we develop a principled Bayesian subtype variant of the event-based model (BEBMS) and compare its performance to SuStaIn in a variety of synthetic data experiments with varied levels of model misspecification. BEBMS substantially outperforms SuStaIn across ordering, staging, and subtype assignment tasks. Further, we apply BEBMS and SuStaIn to a real-world Alzheimer's data set. We find BEBMS has results that are more consistent with the scientific consensus of Alzheimer's disease progression than SuStaIn.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。